System, method, computer program and data set intended to facilitate the comprehension and/or learning of languages by utilizing modified versions

ABSTRACT

The invention relates to a system, method, computer program and data set which are intended to facilitate language comprehension and/or learning. For said purpose, samples of a target language are worked on and modified versions of said samples are used. The purpose of the modified versions is to provide clues which will enable the person using the invention to understand the target language samples. The invention greatly facilitates the management of the modified versions. In particular, the invention aids the tutor in the creation of modified versions and, in addition, in the identification of the versions most suited to the person interested in understanding and/or learning the target language. The modified versions are produced using sets of modifications, referred to herein as Relations, which are processed autonomously.

TECHNICAL AREA

The present invention belongs to the area of aid tools to develop thecomprehension and/or learning of language in general, and of foreignlanguages in particular.

PRIOR ART

The following references show the prior art and also contain informationand knowledge that have been used to develop the present invention:

-   [1] And: “AND Active English”. Multimedia English course.-   [2] Baker, M. C. (2001): “The Atoms of Language”, Basic Books, New    York.-   [3] Barriere, C., Duquette, L. (2002): Cognitive-Based Model for the    Development of a Reading Tool in FSL, “Computer Assisted Language    Learning”, Vol. 15, No. 5, pp. 469-481.-   [4] Davis, D. D. (2002): “El Don de la Dislexia” (The Gift of    Dyslexia), Editex, Madrid, 2000-   [5] Doughty, C. (1991): Second Language Instruction Does Make a    Difference, “Studies on Second Language Acquisition”, 13, pp.    431-469.-   [6] Dr. LANG group: “LANGMaster Courses”. Multimedia English course.-   [7] Gass, S. M., Mackey, A., Pica, T. (1998): The Role of Input and    Interaction in Second Language Education, “The Modern Language    Journal”, 82, pp. 299-307.-   [8] Gross A., Wolff, D. (2001): A Multimedia tool to Develop Learner    Autonomy. “Computer Assisted Language Learning”, Vol 14, No. 3-4,    pp. 233-249.-   [9] Hagoort, P., Brown, C., Groothusen, J. (1993): The Syntactic    Positive Shift (SPS) as an ERP Measure of Syntactic Processing,    “Language and Cognitive Processes”, 8 (4), pp. 439-483.-   [10] Hahne, A., Friederici, A. (1999): Electrophysiological Evidence    for Two Steps in Syntactic Analysis: Early Automatic and Late    Controlled Processes, “Journal of Cognitive Neuroscience”, 11 (2),    pp. 194-205.-   [11] Kim, K. H. S., Relkin, N. R., Lee, K., Hirsch, J. (1997):    Distinct cortical areas associated with native and second languages,    “Nature”, 388, 10 July.-   [12] Krashen, S. (1980): The Input Hypothesis, en J. Alatis (Ed.),    “Current Issues in Bilingual Education”, pp. 144-158, Washington,    D.C.: Georgetown University Press.-   [13] Long, M. (1980): “Input, Interaction, and Second Language    Acquisition”, non published PhD dissertation, University of    California, Los Angeles.-   [14] Loschky, L. (1994): Comprehensible Input and Second Language    Acquisition, “Studies in Second Language Acquisition”, 16, pp.    303-323.-   [15] Nieto, A., Santacruz, R., Hernandez, S., Camacho-Rosales, J.,    Barroso, J. (1999): Hemispheric Asymmetry in Lexical Decisions: The    Effects of Grammatical Class and Imageability, “Brain and Language”,    70, 421-436.-   [16] Oh, S. (2001): Two Types of Input Modification and EFL Reading    Comprehension: Simplification Versus Elaboration, Tesol Quarterly,    Vol. 35, No. 1, Primavera 2001.-   [17] Ortiz, T., Fernández, A., Maestu, F., Amo, C.,    Sequeira, C. (1999) “Magnetoencefalografía”, Center of    Magnetoencefalography Dr. Pérez Modrego, Universidad Complutense de    Madrid.-   [18] Palacios, A. (2003): Patent application ES200302943.-   [19] Palacios, A. (2004): Patent application ES200400030.-   [20] Pinker, S. (1999): “Words and Rules”, London: Weidenfeld &    Nicholson.-   [21] Streb, J., Rösler, F., Hennighausen, E. (1999): Event-related    responses to pronoun and proper name anaphors in parallel and    nonparallel discourse structures, “Brain and Language”, 70, pp.    273-286.-   [22] Transparent Language. “Learn Italian Now”. Multimedia Italian    Course.-   [23] VanPatten, B. (1996): “Input Processing and Grammar    Instruction”, Ablex Publishing Corporation, Norwood, N.J.-   [24] Yano, Y., Long, M. H., Ross, S. (1994): The Effects of    Simplified and Elaborated Texts on Foreign Language Reading    Comprehension, “Language Learning”, 44:2, June, pp. 189-219.

Translation Note: The language examples in this document have beendeveloped for the Spanish language. The examples will be translated intoEnglish whenever the structural features of the example also exist inEnglish. In the cases in which direct translation is not possible, theSpanish example will be maintained, and word by word literal translationwill be provided. This literal translation will be shown below theoriginal Spanish text and will be enclosed by parenthesis.

Language learning is a pressing need in current society but, in spite ofthat, there do not exist methods that can satisfy it efficiently. Bothteachers and learners are still wanting for the solution to thisproblem. And this is happening despite the fact that a lot of scientificand technical knowledge has been created in the last fifty years abouthow the brain manages language.

The last years have witnessed a great increase in the technicalcharacter of the research on language. Numerous technical and scientificresources are being used in order to understand the brain processes thatare related to the learning and working of native and second languages.In this respect, experiments are being carried out withelectroencephalograms, functional magnetic resonance, positron emissiontomography (PET), and magnetoencephalographs. These experiments haveshown that concepts such as “verb”, “sentence”, “semantics” etc. areassociated to well defined and sophisticated electrophysiologicalprocess. For example, in one of these experiments, Streb and hiscolleagues have shown that the electrophysiological processes of thebrain depend on the grammatical categories that are being processed[Streb et al, 1999]. Other references that describe the brain processesthat are related to different aspects of language are the followingones: [Pinker, 1999], [Hagoort et al, 1993], [Hahne and Friederici,1999] y [Nieto et al, 1999].

In relation to second language learning, Kim and his colleagues usedfunctional magnetic resonance images to show that, when individualsspeak a second language, those persons who have learned it duringadulthood use different brain areas than those individuals that havelearned it during childhood [Kim et al, 1997].

The conclusion of this analysis is that the goal in second languagelearning should be to develop systems and methods that help the learnerto develop neurological structures that are similar to the neurologicalstructures that native speakers have. This will allow the secondlanguage learner to have a command of the second language similar tothat of the native speaker.

In this respect, recent research on dyslexia has shown that with certainpsycholinguistic training it is possible to change the neurologicalstructures that individuals use in order to produce language [Ortiz etal, 1999].

In order to help the language learner develop neurological structureswhich are similar to those of the native speakers it is necessary totrain those neurological structures. In order to do that, it isnecessary for the learner to use those structures during learning. Thisis the reason why it is important that the learner does not usetranslation in order to comprehend, because translation utilizesdifferent brain resources. However, normally, many learners usetranslation in order to understand the linguistic messages that theyreceive.

The comprehension of messages that are perceived in the target languageis essential for learning, as modern research on language acquisitionmaintains. The problem is how to comprehend language samples of a targetlanguage without utilizing translation. In what follows, the relationbetween comprehension and learning of a target language will beexplained in more detail, so that the invention of this patentapplication can be better described.

The two main hypothesis about the influence of comprehension in languagelearning are the content hypothesis [Kristen 1980] and the content andinteraction hypothesis [Long 1980], [Loschky 1994].

In general terms, the content hypothesis maintains that languagelearning is based on comprehending messages that are generated in thatlanguage, which will allow to develop the ability to associate form andmeaning. This association ability is the basis for language utilization.In this sense, it has been mentioned that children will only manage tomake progress in breaking the code of a language if they somehow haveaccess to what the sentences that they are listening to mean [Baker2001, p. 224].

The content and interaction hypothesis maintains that the best way tocomprehend messages is by interacting and generating clues thatfacilitate comprehension. This hypothesis is an extension of the contenthypothesis. In this respect, Baker says, referring to television, that amedia that is rich in content but poor in interaction fails in thisaspect, because it does not provide enough visible indications tochildren about what the characters on the screen are saying [Baker 2001,225].

Moreover, the language samples on which the user is working must containlinguistic aspects that the user does not know. Yano et al mention thatif the learner does not receive linguistic aspects that are new, she/hewill not have the opportunity to learn them [Yano et al 1994]. In thisline of thought, Gass mentions that it is non comprehensible messageswhat can generate the recognition that there is a gap between thelinguistic competence of the learner and the characteristics of thetarget language, and that therefore it is necessary to somehowreorganize the linguistic competence [cited in Gass et al 1998].

Therefore, in order to facilitate learning, it is necessary for theuser-learner to perceive and comprehend samples of the language thatshe/he wants to learn, and these samples must contain linguistic aspectsthat have higher complexity than what the learner already masters. Inorder for the learner to comprehend these linguistic aspects that she/hedoes not know, the best situation is one in which the learner interactswith the environments and generates clues about the meaning of thosesamples.

A system that facilitates such comprehension and that can be applied toall type of texts is specially useful for foreign language learning,because it allows the learners to work with authentic texts. Authentictexts are language samples that have been generated to satisfy acommunicative or informative need in the community of native speakers ofthe language in which the texts have been created. Foreign languageteachers are of the opinion that authentic contents are very useful,because they increase the motivation of the leaner and also because theycontain those linguistic structures that are used in real life.

Even though there are several approaches about to how to assist the userto better comprehend language, all of them have limitations. Thosesignificant references that have been found can be organized in thefollowing four groups, depending on what means they use:

-   1. Group a. These approaches provide general indications and    strategies. [Gross, 2001] and [Barriere et al 2002] belong to this    group. The problem with these two references is that they are still    in development, and they do not provide concrete proposals.-   2. Group b. These approaches provide translations of the target    language sample. [Transparent Language] [And] and [Dr. LANG group]    belong to this group. The problem with these proposals is that they    only provide translations, so that the possibility of associating    form and meaning in the foreign language does not exist.-   3. Group c. These approaches provide some type of representation of    the structure of the sample of target language. [Doughty, 1991]    belongs to this group. The main problem of this reference is that it    does not provide a mechanism that can be generalized to all type of    texts.-   4. Group d. These approaches provide modified versions about the    target language, which can be either elaborations or    simplifications. [Yano et al 1994], [Oh 2001] and [Loschky 1994]    belong to this group. The advantage of modified versions is that it    is easier for the user to comprehend them than it is to comprehend    the original versions. The main disadvantage of these references is    that the process to manage modified versions is not systematic.

As has been seen, in general, systems do not exist that facilitate thecomprehension of samples of a target language in an effective way. Oneof the reasons is the difficulty to create a system that can be used ina systematic way with any type of text. Such a system does not existyet, despite the fact that it would be extremely useful for languageteachers and for learners.

It is necessary to develop inventions that facilitate creating thesesystems. The fact that these systems do not exist, despite the fact thatit is known that they would be very useful, shows that the system thatis proposed in this patent application requires a significant inventiveeffort.

EXPLANATION OF THE INVENTION Introduction

The goal of the invention is to facilitate the comprehension and/orlearning of languages with an enhanced system to produce, manage andutilize modified versions. The invention is used in such a way that theuser goes through certain language samples and works on certainfragments in order to understand them. In this invention, in order tofacilitate the exposition, each fragment on which the user is working iscalled Original Extract.

In this invention, modified versions of the Original Extracts are shownto the user, so that such modified versions facilitate the comprehensionof such Original Extract. In this document, such modified versions arecalled Modified Extracts. The number of Modified Extracts that arepresented to the user for each Original Extract will depend on thecomprehension difficulty that the Original Extract presents. It might bepossible that for an Original Extract there exists no Modified Extractand that for a different Original Extract there exists a high number ofModified Extracts.

In order to facilitate the exposition and without limitative effects, inwhat follows it will be assumed that, even though for an OriginalExtract there might be several Modified Extracts, only one is shown tothe user at each particular time. The Modified Extract that is shown tothe user at a particular time is called Current Extract. It is alsoassumed that the user can simultaneously inspect the Original Extract touse it as a reference and compare it with the Modified Extract.

Essence of the Invention

The essence of the invention is to manage the different modifiedversions by means of modifications, where some of these modificationscan be applied in an independent fashion. That is, in the inventionthere exist several possible modifications, and some of thesemodifications can be applied independently in order to generate thedifferent Modified Extracts.

In some cases, it will not be possible to apply some of thesemodifications in an independent way, depending on the design that isapplied to the invention, but this does not limit the advantages nor thenature of the invention. For example, several modifications can belinked in a higher order modification that might require thesimultaneous application of the modifications that belong to it. Forexample, this could be the case if the purpose of a modification ismarking a word, and the modification is made up of two constituentmodifications, one of which turns the format of the word into bold fontand the other one turns it into underlined font.

The approach of the invention is different from current approaches. Inthe current approaches, the focus is put on developing the differentfull modified versions, rather than on the different individualmodifications that generate the modified versions.

As will be seen, the present invention also allows to implement a numberof optional functions that very much facilitate the process ofgeneration and management of modified versions, as is described in thesection in which the preferred embodiment is explained. These advantagesprovide benefits both to the tutor who is in charge of preprocessing thelanguage samples and to the learner.

In this invention, the modifications that are managed in an independentway are called Relations. In general, Relations contain informationabout different aspects of the Original Extract, and about themodifications that can be applied to it in order to make it morecomprehensible. For each Original Extract there might exist a pluralityof Relations, and the number of Relations will depend on the complexityof such Original Extract.

In the simplest case, Relations have two activation levels, whichcorrespond the active and non-active states. In this case, when aRelation is active, the modification contained in the Relation isapplied to the Original Extract, and a Modified Extract is generated.When a Relation is non-active, the modification is removed from theModified Extract.

In the most general case, a Relation might have more than two activationlevels, and there will be a different modification for each activationlevel. When the Relation is activated to its successive possible levels,the modifications that belong to the different levels will beincrementally applied, yielding different Modified Extracts.

Exhibits 1, 2 and 3 show some examples that clarify how Relations areused. For the time being, the only part that is described is what ispresented to the user. Later on in this document the processes to managethe data that make up the Relations will also be explained.

In the example of Exhibit 1, the Original Extract presents acomprehension problem for a language learner, because in the secondcoordinated sentence the verb “went” has been omitted. For this case, aRelation is created whose mission is to insert the word “went” in theappropriate position. In step 1, the relation is non-active, i.e. itsactivation level is “0”, so the Current Extract coincides with theOriginal Extract. When Relation 1 is activated, in step 2, the word“went” is inserted in the Modified Extract. When Relation 1 isdeactivated again, in step 3, the modification disappears and theCurrent Extract gets the form of the Original Extract.

Exhibit 1

Original Extract: John went to Paris and Mary to Chicago

Step Configuration Current Extract 1 Relation 1, level 0. John went toParís and Mary to Chicago 2 Relation 1, level 1. John went to París andMary (went) to Chicago 3 Relation 1, level 0. John went to París andMary to Chicago

In Exhibit 2 there is a comprehension problem because the word “Juan”,which is the subject of the verb “ha venido”, is postponed behind theverb. This might create a comprehension problem because in Spanish thecanonical order is Subject-Verb-Object, and also because in general theuser might expect this Subject-Verb-Object order for any reason (even ifit is in a language in which it is not the canonical order)

In order to solve this problem, a Relation is created that has twoactivation levels.

-   1. Activating the Relation to level 1 will insert the character    string “[@]” in the position that the subject of “ha venido” would    occupy if the structure was the canonical structure. In these    circumstances, the learner would see that the verb “ha venido”    actually has a subject, which should be in the position of the    character string “[@]”, but which is located in a different position    in the sentence.-   2. Activating the Relation to level 2 will replace the character    string “[@]” with the actual subject “Juan”. Now, the learner would    see the subject placed in its canonical position.

Exhibit 2

Original Extract: Pepe ha preguntado si ha venido Juan

-   -   (Pepe has asked whether has come John)

Step Configuration Current Extract 1 Relation 1, level 0. Pepe hapreguntado si ha venido Juan 2 Relation 1, level 1. Pepe ha preguntadosi [@] ha venido Juan 3 Relation 1, level 2. Pepe ha preguntado si(Juan) ha venido

In this invention, the characters that are used to indicate positions,such as “[@]” in the previous example, are called Localizers. Dependingon the actual embodiment, the localizers can be surrounded by certainseparating characters, such as the brackets in the previous example.

The content of the Localizers could be non alphabetic characters, suchas in the previous case, or could be real words of the target language.The latter could happen, for example, in cases in which it is necessaryto move a first word and the destination position is closely relatedwith a second word, in which case the second word might act as aLocalizer.

Exhibit 3 shows an Original Extract that is more complex than the onesthat have been shown thus far. In this case, the complexity has to dowith the fact that there are two aspects that are simultaneously causingcomprehension problems: the subjects of the verbs “Es” and “venga” arenot located in their canonical positions. In order to create modifiedversions that take both aspects into account, two Relations that moveword groups are created. A difficult issue in this respect is that thetwo word groups that must be moved (by each Relation) are sharing aword.

Relation 1 is about the verb “Es” and its subject: “que venga Juan”.Relation 2 is about the verb “venga” and its subject: “Juan”.

When Relation 1 is activated to level 1, the Localizer “[+]” is shown inthe position that the subject of “Es” should occupy, so thismodification will indicate that the verb “Es” has a subject and that itis located somewhere in the sentence. When Relation 1 is activated tolevel 2, the subject of “Es” is moved to replace the Localizer “[+]”.

Relation 2 operates in a similar way. When this Relation is in level 1,the Localizer “[@]” indicates the position that the subject of “venga”should occupy. When the Relation is activated to level 2, the actualsubject replaces the Localizer “[@]”.

The invention facilitates to efficiently manage all these modifications.Because there are two Relations and each Relation has three possiblelevels, in total there might be nine possible modified versions. Withthe invention, it is not necessary to create nine different sentences,because only one sentence is created that can potentially adopt ninedifferent forms.

Exhibit 3

Original Extract: Es estupendo que venga Juan

-   -   (Is great that come John)

Step Configuration Current Extract 1 Relation 1, level 0. Es estupendoque venga Juan Relation 2, level 0. 2 Relation 1, level 1. [+] esestupendo que venga Juan Relation 2, level 0. 3 Relation 1, level 2. Quevenga Juan es estupendo Relation 2, level 0. 4 Relation 1, level 2. Que[@] venga Juan es estupendo Relation 2, level 1. 5 Relation 1, level 2.Que Juan venga es estupendo Relation 2, level 2.

As has been seen before, in general terms, the modifications aregenerally carried out by adding words, removing words, modifying wordsor moving words.

Optional Aspects of the Invention

The invention contains several optional aspects. Some of them aresuccinctly described here, and some of them will be described in moredetail in the exposition of the preferred embodiment.

On the one hand, several optional functions can be added for selectingand activating Relations, which are explained together with thepreferred embodiment.

Also, several graphical means can be used to emphasize different partsof the Extracts, as is done in some examples.

The Localizers can be used with different types of characters, inaddition to the characters “@” y “+”. In general, it would be advisableto always use the same type of character for the same type ofgrammatical structure.

Moreover, the invention can be integrated with other proposals forlanguage learning, such as for example the proposals explained in thepatent applications [Palacios 2003] and [Palacios 2004] given that theseinventions have been developed at the same time.

Comments about the Invention

The invention can be used to help the user to learn a target language,or simply to help her/him to comprehend the target language, or for bothgoals.

The invention can also be used for helping individuals that might haveproblems to fully utilize their own native language. Dyslexicindividuals, for example, seem to suffer several problems which aresimilar to the problems that foreign language learners have, such as forexample difficulties in the comprehension of functional words(functional words are those whose purpose is not to transmit a meaningin itself, but to assist in the global processing of the sentence; forexample, in “the dog”, “the” is a functional word and “dog” is a lexicalword, which does have a meaning by itself [Davis 2002], [VanPatten1996].

In order to use the invention, the tutor must have processed the targetlanguage samples. This processing comprises the steps of defining theOriginal Extracts, modifying the Original Extracts in an appropriateway, and creating the appropriate Relations for each Original Extract.The result of this preparation will be a group of data that will be usedas the basis for generating modified versions.

Many of the actions that the tutor performs in order to process thetarget language sample could be performed automatically. However, inorder to facilitate the exposition, in this document it will be assumedthat all the actions are carried out by the tutor manually.

Advantages of the Invention

The present invention has several advantages over the references thatwere mentioned in the Prior Art section. The main advantages are thefollowing ones:

-   1. It helps the learner in using modified versions

In contrast to the existing proposals in the prior art, this inventionfacilitates that the user learner interacts with language. Given thatthe modifications can be applied at will and independently, the user canfollow her/his own path in activating modifications, so that it iseasier for her/him to detect what aspects of the Original Extract causeproblems, which ease the task of finding the modifications that canbetter solve them.

-   2. It helps the tutor in preparing the modified versions

The tutor has only to generate the different Relations, instead ofcreating numerous modified versions for the same sentence. In the usualapproaches, it is necessary to create as many modified versions as thenumber of combinations of the possible modifications. For example, ifthere are three Relations and each Relation has three possibleactivation levels, the total number of Modified Extracts would be 27.Using the present invention, it would be necessary to create only oneExtract, which would be automatically modified to cover those 27possibilities. Similarly, if the tutor wants to add a new Relation,which also has three activation levels, the traditional approach wouldrequire rewriting three times those 27 previous modified versions.However, with the present invention, it is only necessary to add a newRelation.

Besides that, as will be explained in the description of the preferredembodiment, the tutor can use the invention in a highly systematic wayin order to create modified versions, based on the identification ofindividual modifications.

Furthermore, the individualized and automatic fashion in which Relationsand modified versions are managed allows to easily test different typesthe modifications, to check how useful they are for the user learners.This would allow to choose the most appropriate set of modifications foreach learning level.

DESCRIPTION OF THE FIGURES

FIG. 1 shows a general scheme of the preferred embodiment, which isbased on two computerized systems, the Tool and the Application.

FIG. 2 schematically shows the window of the preferred embodiment inwhich the text is shown that corresponds to the sample of language onwhich the user is working at a given time.

FIG. 3 schematically shows the window of the preferred embodiment inwhich the Original Extract and the Modified Extracts are shown. Thiswindow also contains several controls to manage the Relations.

FIG. 4 shows the window of the preferred embodiment that is used in theTool to create Relations by using Relational Schemes. The sentencesshown in the Figure are the following ones: “Es una pena que venga” (Isa pitty that comes); “Vino Juan” (Came John); “(Juan) corrió treskilómetros” ((John) ran three kilometers).

EXPOSITION OF AN EMBODIMENT OF THE INVENTION

Exposition of the Preferred Embodiment

General Description

Physical Support

As shown in FIG. 1, in the preferred embodiment, the invention is builtby using two systems 140 and 150, both of them being computerizedsystems. System 140 is called Tool, and it will be used by the tutor 120to create the Relations that will later on generate the modifiedversions. System 150 is called Application, and the user learner 130uses it to work on the target language samples 110.

Systems 140 and 150 can be based for example on two Dell® Dimension XPS®computers, to which two mouses and two keyboards are added in order forthe user to carry out the interactions with the system.

Each one of these systems contains an operating system, such as forexample Microsoft® Windows 2000® and a database manager, such as forexample Microsoft Access®.

Furthermore, each system contains a specific computer program that willmanage the interactions performed by the person that uses it, either thelearner or the tutor, and that will allow to create and process theRelations. Such programs can be created for example with the developmentenvironment Microsoft® Visual C++®.

Distribution of Samples of Target Language

The samples of target language 110, independently of what format theyare in (text, audio, sign language . . . ), will be converted into text,and will be presented in a window. In the Tool they are presented to thetutor, and in the Application they are presented to the user learner.Such window is shown in FIG. 2 with an example text.

The tutor structures the samples of target language into OriginalExtracts in such a way that each Original Extract corresponds to asentence. The Original Extracts are stored in a database that isaccessible to the Tool, in a file 160, in such a way that each OriginalExtract corresponds to a record. Each such record contains the main datafor each Original Extract, such as the start position and end positionof the text, besides other data. There also exist a set of Relationswhich are stored in an array Relations( ). The file 160 is transferredto the data file 170 of the Application, in which it is accessible tothe learner so that he/she can use it.

The Application shows the different Original Extracts 180 to thelearner, and if so required, it also shows a plurality of ModifiedExtracts 190 for each one of such Original Extracts.

The Application presents the required information to the user learnerthrough several windows. One of these windows is the Text window, whichhas been shown in FIG. 2 and which allows to select the differentOriginal Extracts. When an Original Extract has been selected, theApplication shows it in the window Extracts 210, which is shown in FIG.3. This window also exists in the Tool, with the purpose of assistingthe Tutor in his/her work.

The Original Extract, which appears in the subwindow 220, will be usedas a reference. The modified versions will be shown in the subwindow230, which shows the Current Extract (i.e. the successive ModifiedExtracts). The Current Extract will initially have the same form as theOriginal Extract, but when the user activates a Relation, the CurrentExtract will change and will adopt the form of one of the ModifiedExtracts.

There also exist several subwindows, such as the subwindows 240 and 270,in which the user learner can interact and manage the Relations andmodified versions, which will be described below.

As was mentioned, the window 200, which shows the text, is shown both inthe Tool and in the Application. Both the tutor and the learner canselect a word in the text, and the system will use that selection inorder to identify the Original Extract to which it belongs and presentthe windows and subwindows that are associated to it.

Selection of Relations

In window 210 there exist two subwindows intended to manage the existingRelations. In subwindow 270 there exist several controls, one of whichis control 280, which shows a list of Relations. In control 280, theRelations that are selected at a given moment are emphasized with somegraphical means, which in the current case is bold font. At the left ofeach Relation, there appears a number that indicates the currentactivation level of that Relation.

Control 290 shows the possible activation levels that the selectedRelation might have. The activation level that the selected Relation hasat a given moment is emphasized with bold font. For the current datashown in the window 210 in FIG. 3, Relation 1 is the selected Relation,and it is not active.

Functions to Assist the Learner in Activating Relations

In the preferred embodiment, there exist certain special functions thatfacilitate utilizing the invention. There also exist differentutilization modes, depending on what functions are used in each case.That is to say, in each mode one of these functions can be used. Thosemodes are not described here in order to ease the exposition.

Recognition Function

This function is based on adding certain information that associatesdifferent fragments of some Extract and on graphically emphasizing someof those fragments when certain interaction takes place. For example, itis possible to encode into the system that a given fragment “A” and agiven fragment “B” are associated by this function. Then, when the userselects fragment “A”, the system will use certain graphical means toemphasize fragment “B” in order to show the association between bothfragments.

In the preferred embodiment, the Recognition function is used with thoseRelations that move words, such as for example in the example that isshown in Exhibit 4. In step 2, when the user selects the character [@],the function will emphasize with bold font the selected character itselfand will also mark with bold font the word “Juan”, to indicate that thisis the word that might take that position. In step 3, when the userclicks on “preguntado”, the emphasis in the previous words iseliminated.

Exhibit 4

Original Extract: Pepe ha preguntado si ha venido Juan

-   -   (Joseph has asked whether has come John)

Step Action Current Extract 1 Initial situation Pepe ha preguntado si[@] ha venido Juan 2 Click on “@” Pepe ha preguntado si [@] ha venidoJuan 3 Click on Pepe ha preguntado si [@] ha venido Juan “preguntado”Jump Function

This function is based on adding certain information to the system thatlinks the selection of certain fragments of the Current Extract with achange of level for some Relation. In the preferred embodiment thisfunction is built in such as way that it contains two types of responsesfor different cases:

-   1. Response 1 takes place when the selection falls on a word or a    group of words that are encoded to perform a level change in the    Relation, so that such level change takes place. Response 1 is used    in Relations that move words using a Localizer. In order to do that,    an order is encoded that says that “every time a Localizer is    selected, the Relation will be automatically activated to the next    level”.-   2. Response 2 takes place when it happens that a group of words is    selected and some of the words in the group them are not visible    (for example a Localizer or another word). In this case, the    Relations to which those hidden words belong get activated up to the    required level to make those hidden words visible. This is only    applied to hidden words that have the appropriate characteristics,    as will be explained later.

Exhibit 5 shows how the process of response 1 would word for an actualexample. In level 0, if the user selects “Juan”, level 1 gets activated,so that the Localizer is shown and “Juan” is emphasized. In level 1,when the user selects the localizer “[@]”, the Relation would getactivated to level 2, so that “Juan” would replace “[@]”.

Exhibit 5

Original Extract: Pepe ha preguntado si ha venido Juan

-   -   (Joseph has asked whether has come John)

Activation Action State Current Extract Initial Situation Level 0 Pepeha preguntado si ha venido Juan Selection on “Juan”, Level 1 getsactivated Level 1 Pepe ha preguntado si [@] ha venido Juan Selection on“@”, Level 2 gets activated Level 2 Pepe ha preguntado si Juan ha venido

Exhibit 6 shows how to use Response 2 in the case of a Relation thatmoves words. The user could select the fragment “si ha venido”, maybebecause he or she has problems in comprehending the role of “ha venido”.In this case, the invention would detect that there exists a hiddenLocalizer and in order to make it visible it would activate the Relationassociated to Level 1.

Exhibit 6

Example Original Extract: Pepe ha preguntado si ha venido Juan

-   -   (Joseph has asked whether has come John)

Activation Action State Current Extract Initial Situation Level 0 Pepeha preguntado si ha venido Juan Selection on “si ha venido”, Level 1gets activated Level 1 Pepe ha preguntado si [@] ha venido JuanBuilding and Processing of Relations for Creating Modified VersionsGeneral Description

An important advantage of the invention is that it greatly facilitatesthe creation of modified versions. This is so because it allows tocreate modifications after a set of entities that are described in thefollowing lines. In this section, a general introduction to thoseentities will be provided, and they will be described in greater detailin the next sections.

-   1. In order to generate modified versions (i.e. the Modified    Extracts), Closed Extracts are utilized. Each Closed Extract is a    text string that contains all the relevant data about an Original    Extract and all the necessary data to generate the modified versions    that can be generated for that Original Extract. In order to define    the modifications that can be applied to an Original Extract, the    tutor generates a set of Relations, as was previously mentioned.-   2. In order to generate each Relation, the tutor uses certain    entities called Relational Schemes. Basically, Relational Schemes    are templates that help to define Relations. Relational Schemes    contain certain variables that are related to the different    characteristics of the Original Extract that is being worked on. The    tutor generates the Relation assigning values to the variables of    the Relational Schemes. One of these variables, for example, can be    a given set of words of the Original Extract; in this case, the    tutor would assign a group of words to that parameter.-   3. In order to create Relational Schemes, Basic Actions are used.    Basic Actions are simple modifications that can be applied upon    certain word groups that are defined for each Relational Scheme.

In what follows, these concepts will be explained in more detail.

1. Generation of Modified Versions after the Closed Extracts

The generation of modified versions uses both the Open Extract and theClosed Extract. For each Current Extract (independently of whether itcorresponds to an Original Extract or to a Modified Extract) there existan Open Extract and a Closed Extract. They can be considered asdifferent versions of the same Current Extract. That is to say, in thepreferred embodiment, a Current Extract only exists in the form of aClosed Extract or of an Open Extract.

-   -   The Open Extract is the character string that is shown to the        user learner. It is a normal text string in the target language,        and it would present certain grammatical, pragmatic or stylistic        comprehension problems.    -   The Closed Extract is the character string that contains the        totality of the data related to the Extract upon which it is        based. It includes all the words that are related to the Extract        to which it is associated. When Relations are applied upon the        Original Closed Extract, Modified Closed Extracts are generated.        Any of the Closed Extracts can be used in order to generate the        corresponding Open Extract. In order to do that, a filtering        process is applied.

In the preferred embodiment, each Closed Extract is basically made up ofa series of tagged words, which would be similar to the tagged texts inXML or HTML. Each one of those words comprises certain attributes, andthe values of those attributes define the way in which those words mustbe processed in order to generate the Open Extract. For example, theword “car” might be represented as a character string such as thefollowing one “<x₁|x₂|x₃|x₄|x₅|coche|x₇|x₈|x₉|x₁₀|x₁₁|>”, where the“x_(i)” represent the values of the different attributes, and theremight be an arbitrary number of attributes.

Generally, a two step process is followed in order to generate thedifferent modified versions:

-   1. Activating the Relations that have been chosen, so that several    modifications will be applied upon the Closed Original Extract,    which yields several Closed Modified Extracts. When all the    Relations are non-active, the Closed Current Extract will be the    same as the Closed Original Extract.-   2. Filtering one or more of those Closed Modified Extracts, to    generate the corresponding Open Modified Extracts, which will be    shown to the user.

In order to generate the Closed Extracts, the tutor starts using theOpen Original Extract and uses the invention in order to label the wordsthat make it up. She/he will also apply several changes to the OriginalExtract, and will add Relations, so that she/he will generate differentClosed Extracts. In all this process, the invention allows to performactions such as tagging, cutting, pasting, editing, and so on, in asimilar way as how it can be done upon HTML or XML text.

2. Building Relations after the Relational Schemes

As was mentioned, in the preferred embodiment, Relations are built usingRelational Schemes. A Relational Scheme contains the following elements:

-   1. Variables, to which some values must be assigned. These variables    can be groups of words on which certain modifications can be    applied, or can also be options about how those modifications are to    be applied.-   2. Modifications, which belong to the Relational Scheme, which are    those modifications that, when the Relation is activated, will be    applied over specific words.

Relational Schemes greatly facilitate the construction of modifiedversions by the tutor. When a tutor finds a new comprehension problemfor which no Relational Scheme exists, he/she can use the invention tocreate a new Relational Scheme, as will be explained later. ThatRelational Scheme then would be added to the system and would beavailable to the tutor or to other tutors for the future.

In the preferred embodiment, when the tutor inspects the OriginalExtract in the Tool, there will exist a window 420 in which all theRelational Schemes will be defined, and which will contain differenttools to create Relations, as shown in FIG. 4.

3. Building Relational Schemes after Basic Actions

In the preferred embodiment, the Relational Schemes are built aftersimpler entities called Basic Actions. Basic Actions are individualoperations that can be applied upon a word or a group of words.

A Relational Scheme is based on the application of the Basic Actionsthat are assigned to that Relational Scheme upon the word groups thathave been chosen. In the preferred embodiment, in order to create aRelational Scheme, it is necessary to follow these steps, or othersimilar ones:

-   1. Choosing the number of activation levels that will exist for the    Relations that are based on that Relational Scheme.-   2. For each level, defining which Basic Actions will be applicable.-   3. For each Basic Action, indicating the configuration parameters of    the Basic Action. These configuration parameters can include the    type of font format that can be applied to emphasize text, the    characters that will be used to mark text (such as quotation marks,    for example), the Localizer word that will be used for movement    actions, and other ones that might be necessary.

A Relational Scheme is codified and stored as a character string.Exhibit 7 shows an example of a possible Relational Scheme. In theexample, “X1” and “X2” represent chains of word codes.

Exhibit 7

Normal Displacement:

-   1_Show_X1/Mark_X1_[_]/Jump_X2-   2_Move_X2_X1/Jump_X2_X1-   3_Recognition_X1_X2

Those relations that are based on this Relational Scheme would have twoactivation levels:

-   1. When activating to the first level, two Basic Actions are    performed:    -   Action “Show”, upon the words referred to by the code chain X1,    -   Action “Mark”, upon the words referred to by the code chain X1;        for this particular case, brackets will be used to mark the word        group (as indicated by the brackets shown in Exhibit 7)-   2. When activating the second level, a Basic Action is performed:    the words referred to by the code chain X2 are moved to the position    next to the chain code X1.

Besides that, this Relational Scheme contains a Recognition Function,which is encoded and stored as a third level. This function indicatesthat if one of the words of the code chain X1 is clicked on, the systemmust mark the words that are associated to the code chain X1 and thosethat are associated to the code chain X2.

In this Relational Scheme, there are also two Jump functions:

-   -   A Jump function generates the activation Level 1. That is to        say, in Level 0, if the user clicks on one of the words that are        included in X2, the Relation will be activated to Level 1.    -   A Jump function generates the activation Level 2. That is to        say, in Level 1, if the user clicks on one of the words that are        included in the code chain X1, or on one of the words in the        code chain X2, the Relation will be activated to Level 2.        Encoding and Storing Relations

In the preferred embodiment, each Relation is encoded into a textstring. That string contains the words that the Relation can modify, themodifications that can be applied upon them, and the way in which thosemodifications will be executed. An example of those text strings isshown in Exhibit 8.

Exhibit 8

1_Normal Displacement_(—)8-9-10_(—)12

In the Relation shown in Exhibit 8, the following components exist:

-   -   The code or index of the Relation: “1”,    -   The type of Relational Scheme in which it is based, called        “Normal Displacement”.    -   Those words that must be moved, which are the words that have        codes “8”, “9” y “10”.    -   The Localizer, which is the word whose code is “12”.        Compounded Relations

In the preferred embodiment there also exist Compounded Relations, whichare built by combining two or more preexisting Relations. That is tosay, there exist Simple Relations, which are directly based onRelational Schemes, and Compounded Relations, which are based on acombination of other preexisting Relations.

In the preferred embodiment, all the Relations that belong to aCompounded Relation must have the same number of activation levels. Ifthis was not the case, the number of activation levels of the CompoundedRelation would be equal to the number of activation levels of theRelation that has the lower number of activation levels.

In general, Compounded Relations can be used, for example, forperforming multiple displacements, such as for example in the Englishsentence “Only then will you find that money cannot be eaten”, whichcould be transformed into “You will find that money cannot be eaten onlythen”. In order to do it, two Relations moving the fragments “Only then”y “will” would be integrated into a compounded Relation.

Some Existing Relational Schemes

In the preferred embodiment there exists a set of predefined RelationalSchemes. In order to keep the exposition simple, only some of them willbe described in this section. The different Relational Schemes that aredescribed have been developed for Spanish. However, these or otherRelational Schemes can be used for other languages. It is understoodthat an expert in languages and informatics can create many otherRelational Schemes, besides the ones shown here, that would also beincluded with the scope of the invention. It is also understood thatchoosing the appropriate Relational Schemes is a design matter.

The different Relational Schemes that are described will be aggregatedinto different groups that share some feature in common.

For each Relational Scheme an example will be shown. The example will bebased on a number of Open Extracts. A Relation that is based on thatRelational Scheme could be applied to the example Open Extracts. TheseOpen Extracts are distributed in vertical form. Some numbers will appearon their left. Those numbers indicate the activation level of theRelation that is being applied to create the Open Extract that is shownin the same line.

For example, Exhibit 9 shows an example for the Original Extract “Johnwent to Paris and Mary to Chicago”. The first line, with number “0”,corresponds to the situation in which the Relation is non-active and thesecond line, with number “1”, corresponds to the Extract in which theRelation is active at level “1”.

Exhibit 9

-   0. John went to Paris and Mary to Chicago-   1. John went to Paris and Mary (went) to Chicago-   Relational Schemes of “Visualization” Type: The purpose of this    group of Relational Schemes is to show words that are omitted from    the Original Extract. In the preferred embodiment, there exist two    Relational Schemes of this type, such as is shown in Exhibit 10.-   1. The Relational Scheme “Visualization 1” corresponds to cases in    which visualizing the omitted words yields as a result a text with a    high degree of correction.-   2. The Relational Scheme “Visualization 2” corresponds to cases in    which visualizing the omitted words is clearly incorrect. As can be    seen, the difference between both Relational Schemes lies in the    characters that are used in order to mark the words that are    visualized.

Exhibit 10

Visualization 1 0. John went to Paris and Mary to Chicago 1. John wentto Paris and Mary (went) to Chicago Visualization 2 0. John got home andread the newspaper. 1. John got home and [John] read the newspaper.Relational Schemes of the “Specialization” Type: The purpose of thisgroup of Relational Schemes is to indicate that there exist certaingroups of words or certain fragments of words that have a specialfunction or a special characteristic. The groups of words or wordfragments can be marked with special marks, so that the user learnerwould understand that that part of the text has a special function. Inthe preferred embodiment, there are two particular Relational Schemes,shown in Exhibit 11, which are applied upon different types of pronouns.

Exhibit 11

Specialization 1.1 0. Juan la ha pintado (John it has painted) 1. Juan-la- ha pintado Specialization 1.2 0. Juan quiere pintarla (John wantsto paint-it) 1. Juan quiere pintar-laRelational Schemes of the “Association” Type: The function of this groupof Relational Schemes is to show the user learner that there existcertain words in the Extract that are related to each other in a directway. In Spanish, this Relational Scheme can be used for pronominalverbs. Exhibit 12 shows that there exist two Relational Schemes for thistype, each of which is related to different ways in which pronominalverbs can be used.

Exhibit 12

Basic Association 1 0. Aquí se come bien (Here eat well) 1. Aquí #se#come bien 2. Aquí #se# #come# bien Basic Association 2 0. Es importantecomportarse bien (Is important behave well) 1. Es importantecomportar#se# bienExisting Basic Actions

The following Basic Actions have been defined for the preferredembodiment:

-   -   Show: The effect of this Action is to make a word or word group        visible in the Current Extract. The information that is required        for this Action is the word or words that the Action will make        visible.    -   Mark: This Action adds two marks, one on each side of the        indicated word or words, such as for example parenthesis,        brackets etc. The information that is required for this Action        is the word or words that are affected by the Action, and the        characters that will be used as marks.    -   Emphasize: This Action changes the font format of the indicated        word or words. The information that is required for this Action        is the word or words that are affected by the Action and the        format that must be applied.    -   Move: This Action moves words to a different position. The        destination position will be adjacent to the position of a        Localizer. The information that is required for this Action is        the word or words that are affected by the Action and the word        that acts as Localizer.

Some Basic Actions can be very similar to some Relational Schemes. Themain difference among them is that the Relational Schemes might containseveral Basic Actions. That is to say, a Relational Scheme that executesa displacement can be based for example on the combination of thefollowing Basic Actions: Action “Show”, in order to show the Localizer,Action “Mark”, in order to mark the localizer, and Action “Move” to movethe words that are involved to the position of the Localizer. Each oneof these Actions can be refined by making particular choices for theLocalizer and the marks.

Executing “Move” Basic Actions

An important technical aspect when applying a Relation is to keepcontrol of the changes that are made with “Move” Basic Actions. In orderto do that, in the preferred embodiment the following steps areperformed:

-   1. When Closed Extracts are created, a Localizer is inserted into    the destination position to which the relevant words will be moved.    That is to say, a tagged word will be inserted whose function is to    indicate that certain words must be moved to that position. The    Localizer can be a non visible word, and can be based on non    alphabetic characters.-   2. When words are moving, a replica of the words that must be moved    is created, and they are placed on the right of the Localizer.-   3. An attribute of the words that were just created is modified, in    order to indicate that these words are replicas. This is performed    by modifying the attribute called “Copy”.-   4. The original words are hidden, and they remain in their original    position.

A difficult problem that exists when moving words is how to coordinatedifferent Move actions that are applied upon the same words. In order tosolve this problem, in the preferred embodiment, the modification thatis applied to the attribute “Copy” is based on adding the index of theRelation that is applied in each case to the value that that attributemight have before, and adding also a dot to separate the new text thatis now added. The index might be for example, the number “1” for aRelation whose index is “1”.

For example, if the current value of the attribute “Copy” is “3.1.0”,this indicates that these words have bee moved by the Relations “1” and“3”, in that order; the character “0” that remains is the default valuefor those words that have not been replicated. If any of these words isnow moved by the Relation “4”, the new value for the attribute would be“4.3.1.0”.

Exhibit 13 shows how to carry out the “Move” Action for a Relation thatbasically contains that Action. The column titled “Closed Extract” showsa simplified version of how the Closed Extracts would evolve in thepreferred embodiment. In this simplified version only some attributesare shown: “Visible”, “Copy” and “Content”. Moreover, the word markshave been inserted into the attribute “Contenf”. The values of theattribute “Visible” are “s” and “n”, where “s” means “visible” and “n”means “non visible”. The values of the attribute “Copy” are chains ofnumbers that are separated by dots. If the value is “0”, it means thatthat word is original and that it is not the result of a replication. Ifthe value are several numbers, there will exist dots that will separatethem, and that will mean that this word is the result of performing asmany replications as the quantity of numbers that exist, except for “0”.

As can be seen in the Exhibit, in several of the steps there exist wordsthat are not visible. In these cases, the value of the attribute“Visible” is “n”. In order to facilitate the exposition of the process,the contents of the words that are visible will be shown in bold font.

As can also be seen, there exist two localizers. Which Localizer belongsto which Relation is not indicated, but the Localizer for Relation 1 is[+] and the Localizer for the Relation 2 is [@].

It can be seen that every time that a “Move” Basic Action is executed,the original words are hidden, and a replica of those words is createdthat is placed at the right of the appropriate Localizer.

It can also be observed that the “Copy” attribute of the word “Juan”which is visible in the fifth step has two codes, because it has beendisplaced twice. If we want to displace it to the position that wouldcorrespond to it if the displacement of Relation 2 had not beenexecuted, the following must be done. First, it is necessary to findanother word that has the same code as “Juan” and that has the samevalues in the attribute “Copy”, except for the number “2”. (In thiscase, the codes of the words have not been shown, but given that thereexists only one word whose content is “Juan” in the Original Extract, itis obvious that the word that is being sought is the second “Juan” inthe Closed Extract of step 5).

It is necessary now to explain a topic related to the function Jump, inthe modality of Response 2. As can be observed in the Exhibit, in step 3there exist two characters “@”, and none of them is visible. If in thesecircumstances the user selects a text fragment that contains the word ofthe character “@” whose “Copy” value is “0”, this word would not becomevisible, because it is a word that has been moved to a differentlocation. However, if the text fragment contains the word of thecharacter “@” whose “Copy” value is “1.0”, this word would becomevisible. That is to say, in “Move” Actions, there always exists a groupof words which are the last ones that have been replicated, even if theyare not visible. These words are the words that would become visiblewith the function Jump.

Exhibit 13

Original Extract: Es estupendo que venga Juan.

-   -   (Is great that come John)

Step Configuration Open Extract Closed Extract 1 Relation 1, Level 0. Esestupendo <n|0|[+]> <s|0|Es> <s|0|estupendo> Relation 2, Level 0. quevenga <s|0|que> <n|0|[@]> <s|0|venga> Juan <s|0|Juan> 2 Relation 1,Level 1. [+] es <s|0|[+]> <s|0|es> <s|0|estupendo> Relation 2, Level 0.estupendo que <s|0|que> <n|0| [@]> <s|0|venga> venga Juan <s|0|Juan> 3Relation 1, Level 2. Que venga <n|0|[+]> <s|1.0|Que> <n|1.0|[@]>Relation 2, Level 0. Juan es <s|1.0|venga> <s|1.0|Juan> <s|0|es>estupendo <s|0|estupendo> <n|0|que> <n|0|[@]> <n|0|venga> <n|0|Juan> 4Relation 1, Level 2. Que [@] <n|0|[+]> <s|1|Que> <s|1.0|[@]> Relation 2,Level 1. venga Juan es <s|1.0|venga> <s|1.0|Juan> <s|0|es> estupendo<s|0|estupendo> <n|0|que> <n|0|[@]> <n|0|venga> <n|0|Juan> 5 Relation 1,Level 2. Que Juan <n|0|[+]> <s|1|Que> <n|1|[@]> Relation 2, Level 2.venga es <s|2.1.0|Juan> <s|1.0|venga> <n|1.0|Juan> estupendo <s|0|es><s|0|estupendo> <n|0|que> <n|0|venga> <n|0|Juan>Exposition of Other Alternative EmbodimentsGeneral

In a possible alternative embodiment, the computerized systems might notcontain a mouse or a keyboard, and the interaction by the tutor or bythe user learner might take place through a different means, such as forexample with a tactile screen or with an optical pen. Moreover, eitherthe mouse or the keyboard might be missing, and the interaction might becarried out with the device that would remain.

Other form to embody the invention is one in which the modified versionsare generated by the tutor utilizing the Tool, and the Application is anon computerized system. The human tutor or an automatic tutor mightgenerate sequences of Original Extracts and Modified Extracts that mightbe shown to the user learner by a system that might be based on a papersupport, such as a book, or based on a TV. In this case the interactionpossibilities would not exist, but the possibilities to create andmanage modified versions, provided by the invention would still remain.

In a different possible embodiment, the Tool and the Application areconnected by some transmission means, such as for example Internet, andthe texts and Extracts are sent by that means.

There exists a plurality of other alternative embodiments that will notbe explained in order not to add complexity to the exposition.

Regarding Relational Schemes, in addition to the Schemes that weredescribed in the previous section, there might exist many other types,some of which are described next.

Relational Schemes of type “Special Association”. In a similar way ashappens with the Relational Schemes of plain Association type, theseschemes also have the purpose to show the user learner that there existdifferent parts in the sentence that are directly related to each other.This Relational Scheme has been created for soft personal pronouns thatperform the function of direct complement, indirect complement orbenefitiary (in Spanish, soft pronouns are those pronouns that behave asclitics, such as “la” in “Juan la vió” [John her saw]). In this type ofRelational Schemes there exist several subtypes. In order not tocomplicate the exposition, only one of the subtypes will be described.As can be observed, different characters are used in order to mark thegroups of words that have some internal association.Subtype “Special Association 1”: This subtype is applied to softpronouns that are performing the function of indirect complement orbenefitiary. In the preferred embodiment, there exist six types ofRelational Schemes that are based on this model, which are shown inExhibit 14. The sentence that is used is an indirect object sentence,but a benefitiary sentence such as “Juan le ha pintado un cuadro aMaría” (John her has painted a painting to Mary) could also be used.

Exhibit 14

Special Association 1.11 Special Association 1.12 0. Juan le ha dado unlibro a María 0. Juan le ha dado un libro (John her has given a book toMary) (John her has given a book) 1. Juan ·le· ha dado un libro aMaría 1. Juan ·le· ha dado un libro 2. Juan ·le· ha dado un libro ‘aMaría’ 2. Juan ·le· ha dado un libro (a María) Special Association 1.21Special Association 1.22 0. Juan quiere darle un libro a María 0. Juanquiere darle un libro (John wants give-her a book to Mary) (John wantsgive-her a book) 1. Juan quiere dar·le· un libro a María 1. Juan quieredar·le· un libro 2. Juan quiere dar·le· un libro ‘a María’ 2. Juanquiere dar·le· un libro (a María) Special Association 1.31 SpecialAssociation 1.32 0. Juan quiere dárselo a María 0. Juan quiere dárselo(John wants give-her-it to Mary) (John wants give-her-it) 1. Juan quieredár·se·lo a María 1. Juan quiere dár·se·lo 2. Juan quiere dár·se·lo ‘aMaría’ 2. Juan quiere dár·se·lo (a María)Relational Schemes of type Direct Displacement: The purpose of thisRelational Scheme is to directly displace a group of words to adifferent position, without showing the Localizer. Exhibit 15 describeshow this Relational Scheme would be used.

Exhibit 15

Direct Displacement 1 0. Los martes Juan trabaja en casa (The TuesdaysJohn words at home) 1. Juan trabaja en casa los martesRelational Schemes of type Double Displacement. The purpose of thisRelational Scheme is to directly and simultaneously displace two groupsof words. For example, it could be used for cases such as the onerepresented in Exhibit 16.

Exhibit 16

Double Displacement 1 0. Entonces entró Juan (Then came John) 1. Juanentró entoncesRelational Schemes of type Normal Displacement: The purpose of this typeof Relational Schemes is to clarify the contribution to the sentencemeaning of a word or a word group that is occupying a position that isdifferent from the one that the learner would expect. Some previousexamples have been based on this Relational Scheme. The RelationalSchemes of this type have two activation levels. In Level 1, the onlything that is done is indicating the new position of the words that areinvolved. A Localizer is used for that. In Level 2, the words that areinvolved get moved. In general, the Localizer is implemented with acharacter that is not alphanumeric. There are several Relational Schemesthat have this type, and Exhibit 17 shows three of them.

Exhibit 17

Normal displacement 1 0. Ha preguntado dónde vive Juan (Has asked wherelives John) 1. Ha preguntado dónde [@] vive Juan 2. Ha preguntado dóndeJuan vive Normal displacement 2 0. Es bueno que venga Juan (Is good thatcomes John) 1. [+] es bueno que venga Juan 2. Que venga Juan es buenoNormal displacement 3 0. Es bueno comer verduras (Is good eatingvegetables) 1. [↑] es bueno comer verduras 2. Comer verduras es bueno

The invention claimed is:
 1. A system for helping the understanding of atarget language by generating and presenting one or more modifiedversions for at least one sample of said language target, wherein saidsystem comprises the following: a memory, a processing unit, a display,at least a closed extract stored in said memory, wherein said closedextract is a data set that contains references to the words of saidsample, and optionally also references to additional words orlocalizers, wherein a localizer is a string of symbols, said string ofsymbols not being a word in said target language, at least two relationsstored in said memory, wherein a relation is a data set that comprisesthe information required to perform one or more modifications upon saidclosed extract, computer executable instructions that allow saidprocessing unit to: activate at least one of said relations, whereinsaid activation applies to said closed extract the one or moremodifications that are associated to said one relation, said applicationproducing a second closed extract, this second closed extract being amodified closed extract, cause said processing unit to filter at leastone of said closed extracts to create an open extract, said open extractbeing a text fragment in the target language optionally containing inaddition one or more localizers, wherein an open extract that isproduced after filtering a modified closed extract is one of saidmodified versions of said sample, present said open extract in saiddisplay, wherein at least one of said modified versions differs fromsaid sample in one of the ways included in the following plurality ofways: it contains additional words, or it contains some of the samewords in different order, or it contains localizers, so that presentingsaid one or more modified versions provides an aid to the user forunderstanding said sample.
 2. A system as claimed in claim 1, wherein atleast one of said relations comprises more than one activation level, sothat different modifications are applied to one of said closed extractswhen said relation is activated to different activation levels.
 3. Asystem as claimed in claim 1, further comprising: a list of modificationtypes, wherein a modification type is an data entity that defines whatmodification would be performed upon one or more fragments of closedextracts, at least one relational scheme, wherein a relational scheme isa data set that performs as a template, said relational schemecontaining at least two parts, the first one of said parts defining oneof said modification types, and the second one of said parts being avariable that can be assigned to one fragment of said closed extracts,computer executable instructions that allow said processing unit to:present said relational scheme in said display, detect a user action,said action assigning a specific fragment of said closed extract to saidvariable, and create a new relation, assigning said relational schemeand said fragment to said relation, so that this system provides an aidto the user to create new relations.
 4. A system as claimed in claim 3,wherein at least one of said relational schemes comprises severaldifferent modification types.
 5. A system as claimed in claim 3, whereinsaid list of modification types comprises one or more of the followingmodification types: a “show” modification type, wherein applying thisbasic action upon a fragment of said closed extract makes said fragmentto be visible when said filtering is applied to said closed extract, a“move” modification type, wherein applying this basic action upon afragment of said closed extract modifies the resulting position of saidfragment when said filtering is applied to said closed extract, a “mark”modification type, wherein applying this basic action upon a fragment ofsaid closed extract applies a graphical marking upon said fragment whensaid filtering is applied to said closed extract.
 6. A system as claimedin claim 1 further comprising computer executable instructions thatallow said processing unit: create a replica of at least one sourcefragment of said closed extract, insert said replica into an arbitraryposition in said closed extract, assign an identifying code to saidreplica, and filter said blind extract to create an open extract, insuch as way that said source fragment is not shown in said open extract,and said replica is shown in said open extract thereby giving theimpression that said source fragment has been moved, wherein saididentifying code allows to identify the different replicas that arecreated when, in a second step, said replica is used as source fragmentand replicated.
 7. A system as claimed in claim 1, further comprisingcomputer executable instructions that allow said processing unit to:associate one of said relations to one fragment of said extract activatesaid relation when the user performs an interaction action upon saidfragment.
 8. A method for helping the understanding of a target languageby generating and presenting one or more modified versions for at leastone sample of said target language, said method being executed upon acomputer, said method comprising the following steps: providing at leasta closed extract stored in said computer, wherein said closed extract isa data set that contains references to the words of said sample, andoptionally also references to additional words or localizers, wherein alocalizer is a string of symbols, said string of symbols not being aword in said target language, providing at least two relations stored insaid computer, wherein a relation is a data set that comprises theinformation required to perform one or more modifications upon saidclosed extract, activating at least one of said relations, wherein saidactivation applies to said closed extract the one or more modificationthat are associated to said one relation, said application producing asecond closed extract, this second closed extract being a modifiedclosed extract, filtering at least one of said closed extracts to createan open extract, said open extract being a text fragment in the targetlanguage optionally containing in addition one or more localizers,wherein an open extract that is produced after filtering a modifiedclosed extract is one of said modified versions of said sample,presenting said open extract in said display, wherein at least one ofsaid modified version differs from said sample in one of the waysincluded in the following plurality of ways: it contains additionalwords, or it contains some of the same words in different order, or itcontains localizers, so that presenting said one or more modifiedversion provides an aid to the user for understanding said sample.
 9. Amethod as claimed in claim 8, wherein at least one of said relationscomprises more than one activation level, so that differentmodifications are applied to one of said closed extracts when saidrelation is activated to different activation levels.
 10. A method asclaimed in claim 8, further comprising the steps of: providing a list ofmodification types, wherein a modification type is a data entity thatdefines what modification would be performed upon one or more fragmentsof closed extracts, providing at least one relational scheme, wherein arelational scheme is a data set that performs as a template, saidrelational scheme containing at least two parts, the first one of saidparts defining one of said modification types, and the second one ofsaid parts being a variable that can be assigned to one fragment of saidclosed extracts, presenting said relational scheme in said display,detecting a user action, said action assigning a specific fragment ofsaid closed extract to said variable, and creating a new relation, saidcreation-assigning said relational scheme and said fragment to saidrelation, so that this method provides an aid to the user to create newrelations.
 11. A method as claimed in claim 10, wherein at least one ofsaid relational schemes comprises several different modification types.12. A method as claimed in claim 8, wherein said list of modificationtypes comprises one or more of the following modification types: a“show” modification type, wherein applying this basic action upon afragment of said closed extract makes said fragment to be visible whensaid filtering is applied to said closed extract, a “move” modificationtype, wherein applying this basic action upon a fragment of said closedextract modifies the resulting position of said fragment when saidfiltering is applied to said closed extract, a “mark” modification type,wherein applying this basic action upon a fragment of said closedextract applies a graphical marking upon said fragment when saidfiltering is applied to said closed extract.
 13. A method as claimed inclaim 8 further comprising the steps of: creating a replica of at leastone source fragment of said closed extract, inserting said replica intoan arbitrary position in said closed extract, assigning an identifyingcode to said replica, and filtering said blind extract to create an openextract, in such as way that said source fragment is not shown in saidopen extract, and said replica is shown in said open extract, therebygiving the impression that said source fragment has been moved, whereinsaid identifying code allows to identify the different replicas that arecreated when, in a second step, said replica is used as source fragmentand replicated.
 14. A method as claimed in claim 8, further comprisingthe steps of associating one of said relations to one fragment of saidextract activating said relation when the user performs an interactionaction upon said fragment.
 15. A non-transitory computer readable mediumcontaining computer executable instructions that, when executed by oneor more processors of a computer, allow said one of more processors toexecute the following steps: providing at least a closed extract storedin said computer, wherein said closed extract is a data set thatcontains references to the words of a language sample of a targetlanguage, and optionally also contains references to additional words orlocalizers, wherein a localizer is a string of symbols, said string ofsymbols not being a word in said target language, providing at least tworelations stored in said computer, wherein a relation is a data set thatcomprises the information required to perform one or more modificationsupon said closed extract, activating at least one of said relations,wherein said activation applies to said closed extract the one or moremodification that are associated to said one relation, said applicationproducing a second closed extract, this second closed extract being amodified closed extract, filtering at least one of said closed extractsto create an open extract, said open extract being a text fragment inthe target language optionally containing in addition one or morelocalizers, wherein an open extract that is produced after filtering amodified closed extract is a modified version of said sample, presentingsaid open extract in said display, wherein said modified version differsfrom said sample in one of the ways included in the following pluralityof ways: it contains additional words, or it contains some of the samewords in different order, or it contains localizers, so that presentingone or more modified versions helps the user to understand said sample,and said method provides an aid to the user for understanding saidsample.
 16. A non-transitory computer readable medium as claimed inclaim 15, wherein at least one of said relations comprises more than oneactivation level, so that different modifications are applied to one ofsaid closed extracts when said relation is activated to differentactivation levels.
 17. A non-transitory computer readable medium asclaimed in claim 15, wherein said computer executable instructions allowsaid one or more processor to further perform the following steps:providing a list of modification types, wherein a modification type is adata entity that defines what modification would be performed upon oneor more fragments of closed extracts, providing at least one relationalscheme, wherein a relational scheme is a data set that performs as atemplate, said relational scheme containing at least two parts, thefirst one of said parts defining one of said modification types, and thesecond one of said parts being a variable that can be assigned to onefragment of said closed extracts, presenting said relational scheme insaid display, detecting a user action, said action assigning a specificfragment of said closed extract to said variable, and creating a newrelation, said creation assigning said relational scheme and saidfragment to said relation, so that this steps provides an aid to theuser to create new relations.
 18. A non-transitory computer readablemedium as claimed in claim 17, wherein at least one of said relationalschemes comprises several different modification types.
 19. Anon-transitory computer readable medium as claimed in claim 15, whereinsaid list of modification types comprises one or more of the followingmodification types: a “show” modification type, wherein applying thisbasic action upon a fragment of said closed extract makes said fragmentto be visible when said filtering is applied to said closed extract, a“move” modification type, wherein applying this basic action upon afragment of said closed extract modifies the resulting position of saidfragment when said filtering is applied to said closed extract, a “mark”modification type, wherein applying this basic action upon a fragment ofsaid closed extract applies a graphical marking upon said fragment whensaid filtering is applied to said closed extract.
 20. A non-transitorycomputer readable medium as claimed in claim 15, wherein said computerexecutable instructions allow said one or more processor to furtherperform the following steps: creating a replica of at least one sourcefragment of said closed extract, inserting said replica into anarbitrary position in said closed extract, assigning an identifying codeto said replica, and filtering said blind extract to create an openextract, in such as way that said source fragment is not shown in saidopen extract, and said replica is shown in said open extract, therebygiving the impression that said source fragment has been moved, whereinsaid identifying code allows to identify the different replicas that arecreated when, in a second step, said replica is used as source fragmentand replicated.